Introduction to Deep Learning with TensorFlow 2.0

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz Language: English | VTT | Size: 2.35 GB | Duration: 5.5 hours What you'll learn TensorFlow 2.0 Gradient Descent Algorithm Create Pipeline regression model in TensorFlow Lasso Regression Feature Selection with lasso Programming in TensorFlow 2.0 Selection of Penalty factor lambda Visualizing graph in TensorBoard Neuron or Perceptron Model Architecture Loss or Cost Function TensorFlow Keras API Linear Regression Create customized model in TensorFlow Exploratory Data Analysis Data Preprocessing Multiple Linear Regression in TensorFlow Requirements Beginner to Python Description In this course, you will learn advanced linear regression technique process and with this you can able to build any regression problem. Starting from TensorFlow 2.x Linear Regression Gradient Descent Algorithm With this intuition we will work on project: Customer Revenue Prediction. Problem Statement: A large child education toy company which sells educational tablets and gaming systems both online and in retail stores wanted to analyse the customer data. The goal of the problem is determine the following objective as shown below. Data Analysis & Preprocessing: Analyze customer data and draw the insights w.r.t revenue and based on the insights we will do data preprocessing. In this module you will learn the following. Necessary Data Analysis Multi-colinearity Factor Analysis Feature Engineering: Lasso Regression Identify optimal penalty factor Feature Selection Pipeline Model Evaluation We will start with basic of tensorflow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent. Who this course is for: Anyone who want to build and train their own network Curious of data science Who want to learning Deep Learning Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

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